The high-frequency (HF) band, which extends from 3 MHz to 30 MHz, provides a unique environment within which to engage in data communications. This band provides a highly desirable environment for long-haul communications where RF signals need to propagate from a transmitter over the horizon, In the HF band, communication is not limited to line-of-sight applications because a large portion of the energy in an HF signal is reflected by the ionosphere back to the earth and by the earth back to the ionosphere. A receiver located beyond the horizon from a transmitter receives an HF signal because the HF signal bounces between the ionosphere and the earth until it reaches the receiver.
But the HF band also provides a particularly harsh environment within which to engage in data communications. As a result, conventional data communication systems have been limited to transmitting over only lower data rate channels, typically less than 9600 baud, and over channels that have been notoriously unreliable.
Multipath can foe particularly severe when compared to communications in higher frequency bands. Multipath results from different portions of a transmitted wireless RF signal propagating along different paths to the receiver, causing a signal which is broadcast at a precise instant in time to foe received spread over a period of time. In the HF environment, the shortest path may foe a direct line-of-sight path or a single reflection off the ionosphere, and longer paths may result from numerous reflections between the ionosphere and the earth. As a result, a typical multipath scenario in the HF band causes interference, including intersymbol interference (ISI) and other types of interference, over a very large duration of 2-8 ms.
And, the ionosphere is constantly changing to provide other deleterious effects. For example, a few hertz of Doppler is often present in a received HF signal. And, the changing ionosphere changes the nature of HF signal reflections from the ionosphere, causing severe fading, both by itself and through the continually changing nature of multipath.
Conventional receivers intended to receive HF signals have included an equalizer to combat the severe multipath and fading characteristics of the HF channel. One particularly desirable technique uses a decision feedback equalizer (DFE) to filter a baseband form of the received signal. But the DFE must be told how to equalize the received signal to compensate for the HF channel. In order to determine how to equalize the received signal, a channel estimation filter structure is used to estimate channel characteristics, and from the estimates generated by the channel estimation filter the DFE is told how to equalize the received signal. In other words, the multipath and fade characteristics of the channel are modeled as a filter, and when a filter definition that models the channel is determined, that filter definition is mapped into DFE coefficients.
The channel estimation filter operates in accordance with an adaptation algorithm which causes the channel estimation filter to function as an adaptive estimator. But conventional channel estimation techniques have failed to adequately estimate HF channels, resulting in unreliable communications and/or communications at undesirably low data rates or undesirably poor bit error rates (BER).
One conventional channel estimation filter technique uses a Least-Mean-Square (LMS) adaptation algorithm. But the LMS adaptation algorithm is slow to converge and does a poor job of tracking time-varying HF channels. Even when preambles embedded within a transmitted data stream are undesirably long, the LMS adaptation algorithm can fail to converge altogether. Excessively long preambles are undesirable because they lead to slower data communication rates, ail other parameters remaining constant. Accordingly, the poor convergence speed and tracking ability of the LMS adaptation algorithm cause conventional channel estimation filters to make poor estimates of the HF channel characteristics, which causes the DFE to be improperly told how to equalize the received signal, which results in poor performance.
Another conventional channel estimation filter technique uses a Recursive Least-Squares (RLS) adaptation algorithm. The RLS algorithm improves upon the LMS algorithm for the HF channel estimation application because it can achieve a fast convergence. But it still suffers from a poor ability to track the time-varying HF channel. And, its ability to achieve a fast convergence comes at a high cost in computational complexity and in sensitivity to numerical instability. Due to the poor ability to track the time-varying HF channel, the RLS adaptation algorithm causes conventional channel estimation filters to make poor estimates of the HF channel characteristics, which causes the DFE to be improperly told how to equalize the received signal, which results in poor performance. And, the computational complexity has been so great as to cause the algorithm to be implemented in dedicated hardware rather than software programming in a digital signal processor (DSP) or like programmable device, causing increased development costs, increased manufacturing costs, and increased power consumption. The computational complexity has also precluded implementation of an RLS adaptation algorithm in a software-defined radio (SDR).